2025 Conference Objects and Reports

Designing Optimal Dynamic Treatment Regimes Using TMLE for Personalized Math Course-Taking Plans

Pan, Chenguang; Suk, Youmi

This study provides an approach to designing optimal dynamic treatment regimes (DTRs) using Targeted Maximum Likelihood Estimation (TMLE), coupled with ensemble learning algorithms, to build a personalized recommendation model for high school math course-taking plans. Our method uses backward induction and feasibility constraints to create personalized, data-driven recommendations under practical considerations. Our simulation study demonstrates that the proposed DTR-TMLE method yields more accurate recommendations compared to Q-learning based on linear regression. We apply the TMLE method to design math course recommendations using data from the High School Longitudinal Study of 2009 (HSLS:09), ultimately aiming to recommend the right math course for each student at the right time.

Keywords: Optimal Dynamic Treatment Regimes (DTRs), Targeted Maximum Likelihood Estimation (TMLE), Personalized Education, Causal inference